Latent user communities

ABSTRACT

A method implemented by at least one server computer is provided, including: providing, over the Internet, access to a plurality of topics, wherein each topic includes, and further provides access to, a plurality of posted items; recording interaction data for the plurality of topics, the interaction data identifying user activity occurring within each of the topics; analyzing the interaction data to identify clusters of topics that exhibit similar behavioral patterns; for each cluster of topics, generating a community that includes the topics in the cluster; providing, over the Internet, access to the communities, wherein accessing a given community further provides access to the topics included in that community, which further provide access to the posted items that are included in the topics within that community.

RELATED APPLICATION

This application claims priority to and is a continuation of U.S.application Ser. No. 15/446,913, filed on Mar. 1, 2017, entitled “LATENTUSER COMMUNITIES”, which is incorporated herein.

BACKGROUND

The present disclosure relates to methods and systems for determininglatent user communities based on online activity.

Community based recommendation of content is an interesting and activearea of research. Such recommendations often drive user engagement withthe platform of discussion (e.g., Reddit, Yahoo! Newsroom). Existingplatforms of discussion such as Reddit, Digg, and other moderated forumsdo not leverage the temporal dynamics of a platform to improvetopic/community recommendation for existing or new users.

It is in this context that implementations of the disclosure arise.

SUMMARY

Community based recommendation of content is an interesting and activearea of research, as such recommendations often drive user engagementwith the platform of discussion (e.g., Reddit). Implementations inaccordance with the present disclosure provide a temporally-dynamiccommunity-driven exploration method for users. Latent social networkdynamics are analyzed to enable grouping of topics (e.g. “Vibes” inYahoo! Newsroom) into high-level communities. These communities arelatent in nature because they reflect underlying social connections thatbind the users. It is possible to model these connections by observingcommunity activity (e.g., volume of posts, social signals, topicaldistribution of comments) triggered by real-life events. A community canbe considered as a cluster or collection of topics where a new topic canbe added or removed from a given community as time progresses. Suchmodeling effectively captures the dynamics of an ever-evolving socialnetwork. The community can have a label assigned by the editors or evenby the users themselves.

By understanding user behavior through identification of latentcommunities, it is possible to provide community-based contentrecommendation solutions. Broadly, this could mean several things, forexample: (a) recommending specific posts, (b) recommending topics instrongly connected communities that the user is following, and (c)allowing users to follow communities (dynamic sets of changing topics).The latter of these is significant in that there is no existing notionof collections of related topics that are generated from latentcommunity identification as in implementations of the presentdisclosure.

Additionally, implementations of the present disclosure enable modelingand visualization of the latent communities that make up the users andtopics. This can be used both for informational dashboards andexploratory analysis.

In some implementations, a method implemented by at least one servercomputer is provided, including: providing, over the Internet, access toa plurality of topics, wherein each topic includes, and further providesaccess to, a plurality of posted items; recording interaction data forthe plurality of topics, the interaction data identifying user activityoccurring within each of the topics; analyzing the interaction data toidentify clusters of topics that exhibit similar behavioral patterns;for each cluster of topics, generating a community that includes thetopics in the cluster; providing, over the Internet, access to thecommunities, wherein accessing a given community further provides accessto the topics included in that community, which further provide accessto the posted items that are included in the topics within thatcommunity.

In some implementations, analyzing the interaction data includes:determining a covariance amongst the plurality of topics using theinteraction data; generating a sparse graph using the determinedcovariance; processing the sparse graph to identify the clusters oftopics that exhibit similar behavioral patterns.

In some implementations, determining the covariance amongst theplurality of topics includes generating a covariance matrix using theinteraction data; wherein generating the sparse graph includes applyinga graphical lasso to the covariance matrix; wherein processing thesparse graph includes applying a stochastic block model to the sparsegraph.

In some implementations, the interaction data defines a time-dependentmetric for user activity within the plurality of topics, and wherein thesimilar behavioral pattern for a given cluster of topics is defined bysimilar changes in the time-dependent metric occurring over time for thegiven cluster of topics.

In some implementations, the interaction data includes a number of oneor more of active users, posted items, comments, votes, positiveendorsements, negative endorsements, flags, moderator actions.

In some implementations, providing access to a given topic includesproviding an interface for posting items within the given topic, and forinteracting with posted items in the given topic, and wherein theinteraction data includes data obtained via the interface.

In some implementations, the user-posted items include news articles.

In some implementations, a method implemented by at least one servercomputer is provided, including: providing, over the Internet, access toa plurality of topics, wherein each topic includes, and further providesaccess to, a plurality of posted items; recording interaction data forthe plurality of topics, the interaction data identifying user activityoccurring within each of the topics; analyzing the interaction data toidentify clusters of topics that exhibit similar behavioral patterns;using the identified clusters of topics to identify topics forrecommendation to a user; presenting, over the Internet, the topicsidentified for recommendation in a session for the user.

In some implementations, the topics for recommendation are identifiedbased on identifying a topic to which the user has subscribed, andidentifying further topics that are within a same cluster as the topicto which the user has subscribed.

In some implementations, analyzing the interaction data includes:determining a covariance amongst the plurality of topics using theinteraction data; generating a sparse graph using the determinedcovariance; processing the sparse graph to identify the clusters oftopics that exhibit similar behavioral patterns.

In some implementations, determining the covariance amongst theplurality of topics includes generating a covariance matrix using theinteraction data; wherein generating the sparse graph includes applyinga graphical lasso to the covariance matrix; wherein processing thesparse graph includes applying a stochastic block model to the sparsegraph to generate clusters.

In some implementations, the interaction data defines a time-dependentmetric for user activity within the plurality of topics, and wherein thesimilar behavioral pattern for a given cluster of topics is defined bysimilar changes in the time-dependent metric occurring over time for thegiven cluster of topics.

In some implementations, the interaction data includes a number of oneor more of active users, posted items, comments, votes, positiveendorsements, negative endorsements, abuse flags, moderator actions.

In some implementations, providing access to a given topic includesproviding an interface for posting items within the given topic, and forinteracting with posted items in the given topic, and wherein theinteraction data includes data obtained via the interface.

In some implementations, the user-posted items include news articles.

In some implementations, a method implemented by at least one servercomputer is provided, including: providing, over the Internet, access toa plurality of topics, wherein each topic includes, and further providesaccess to, a plurality of posted items; recording interaction data forthe plurality of topics, the interaction data identifying user activityoccurring within each of the topics; analyzing the interaction data toidentify clusters of topics that exhibit similar behavioral patterns;using the identified clusters to recommend specific user-posted items.

In some implementations, using the identified clusters to recommendspecific user-posted items further includes, identifying a first topicto which a user has subscribed, identifying a second topic that iswithin a same cluster as the first topic, and recommending in a sessionof the user a posted item from the second topic.

In some implementations, analyzing the interaction data includes:determining a covariance amongst the plurality of topics using theinteraction data; generating a sparse graph using the determinedcovariance; processing the sparse graph to identify the clusters oftopics that exhibit similar behavioral patterns.

In some implementations, determining the covariance amongst theplurality of topics includes generating a covariance matrix using theinteraction data; wherein generating the sparse graph includes applyinga graphical lasso to the covariance matrix; wherein processing thesparse graph includes applying a stochastic block model to the sparsegraph.

In some implementations, the interaction data defines a time-dependentmetric for user activity within the plurality of topics, and wherein thesimilar behavioral pattern for a given cluster of topics is defined bysimilar changes in the time-dependent metric occurring over time for thegiven cluster of topics.

Other aspects of the disclosure will become apparent from the followingdetailed description, taken in conjunction with the accompanyingdrawings, illustrating by way of example the principles of thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 illustrates an interface for browsing topics in an applicationconfigured for presenting Internet content/articles to a user, inaccordance with implementations of the disclosure.

FIG. 2 illustrates an interface showing presentation of content within atopic, in accordance with implementations of the disclosure.

FIG. 3 illustrates a method for identifying latent communities based ontopical activity, in accordance with implementations of the disclosure.

FIG. 4 is a graph visualizing a plurality of communities, in accordancewith implementations of the disclosure.

FIG. 5 conceptually illustrates the grouping of topics into variouscommunities, in accordance with implementations of the disclosure.

FIG. 6 illustrates a system for providing community-based solutions in asocial sharing platform, in accordance with implementations of thedisclosure.

FIG. 7 illustrates an implementation of a general computer system, inaccordance with an implementation of the disclosure.

DETAILED DESCRIPTION

The following implementations describe systems and methods fordetermining latent user communities based on online activity. It will beobvious, however, to one skilled in the art, that the present disclosuremay be practiced without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present disclosure.

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example implementations.Subject matter may, however, be embodied in a variety of different formsand, therefore, covered or claimed subject matter is intended to beconstrued as not being limited to any example implementations set forthherein; example implementations are provided merely to be illustrative.Likewise, a reasonably broad scope for claimed or covered subject matteris intended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,implementations may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one implementation” as used herein does notnecessarily refer to the same implementation and the phrase “in anotherimplementation” as used herein does not necessarily refer to a differentimplementation. It is intended, for example, that claimed subject matterinclude combinations of example implementations in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

Community based recommendation of content is an interesting and activearea of research. Such recommendations often drive user engagement withthe platform of discussion (e.g., Reddit, Yahoo! Newsroom). Existingplatforms of discussion such as Reddit, Digg, and other moderated forumsdo not leverage the temporal dynamics of a platform to improvetopic/community recommendation for existing or new users.

Implementations of the present disclosure provide a temporally-dynamiccommunity-driven exploration method for users. Latent social networkdynamics are identified to enable grouping of topics (e.g. “Vibes” inYahoo! Newsroom) into high-level communities. These communities are“latent” in nature because they reflect underlying social connectionsthat bind the users. These connections can be modeled by observingcommunity activity (e.g., volume of posts, social signals, topicaldistribution of comments) triggered by real life events. A community canbe considered as a cluster or collection of topics where a new topic canbe added or removed from the community as the time progresses. Thiseffectively captures the dynamics of an ever-evolving social network.The community can have a label assigned by the editors or even by theusers themselves.

By understanding such user behavior, it becomes possible to provide alatent community based content recommendation solution. Broadly this canmean several things, for example: recommending specific posts,recommending topics in strongly connected communities that the user hassubscribed to, and allowing users to subscribe to communities (dynamicsets of changing topics). For example, a “Politics” community maycontain several related topics such as “Donald Trump,” “HillaryClinton,” “Bill Clinton,” “Debate 2016,” etc. Similarly, a post which isposted to the “Donald Trump” topic can be posted to topics that belongto the same community with higher confidence. This avoids the problem ofone having to “repost” a post from a “Donald Trump” topic to, forexample, an “Elections” topic manually. This could be easily avoided byposting a post to a Community that includes both topics, instead of to aspecific topic only.

Implementations of the present disclosure enable modeling andvisualization of the latent communities that are defined by the usersand topics. This can be used both for informational dashboards andexploratory analysis. This could also be used to optimize the placementof topics next to each other or avoid placing potentially unrelatedtopics next to each other (e.g. avoiding placing “Yahoo” and “Google”topics next to “War on Drugs” and/or “Al Qaeda” topics).

Broadly speaking, a method in accordance with implementations of thedisclosure, for identifying latent user communities based ontopic-related user activity, includes the following steps: extract dailyactivity of users from different topics to construct a time series foreach topic over a period of time; construct a covariance matrix based onthe time-series for these topics; apply graph-lasso to the matrix toestimate a sparse graph for the topics; apply block-stochastic algorithmto split the graph into clusters, so that topics that are similar (byuser activity) will be clustered together. The block-stochasticalgorithm assigns soft-membership to each topic with a posteriorprobability, and therefore one can loosely group a topic under severalcommunities.

This method provides several advantages over prior art methods. Byautomatically learning the structure of the underlying latentcommunities, the system can do a significantly better job at bothrecommending topics that a user might be interested in following and inranking user posts. These improvements lead to increased productperformance and better user retention.

Further, because the system observes user activity over time, i.e., itis temporally-dynamic, the system can capture the dynamics of anever-evolving social network. Existing systems require the use of humaneditors to accomplish this. Having an algorithmic-based solution is bothfaster and more cost efficient.

Existing social networks that encourage users to share/comment onarticles do not recommend temporally evolving communities (e.g. Reddit,Digg, Quora, Stumbleupon, Buzzfeed, etc.). By contrast, implementationsin accordance with the present disclosure provide an exploration methodfor users that implicitly recommends temporally evolving communitiesthat consist of topics. In some implementations, topics are generated byeditors. In some implementations, topics can be generated by users ofthe social sharing platform.

FIG. 1 illustrates an interface for browsing topics in an applicationconfigured for presenting Internet content/articles to a user, inaccordance with implementations of the disclosure. In the illustratedimplementation, a screenshot 100 shows an interface from the Yahoo!Newsroom application for mobile devices, which is one example of anapplication configured to present Internet content/articles to users. Inthe present view of the interface, various topics are graphicallyrepresented by icons. For example, the icon 200 represents the topic“Grammys.”

For purposes of the present disclosure, a topic will be understood as asubject identifier for social sharing of Internet content/articles (e.g.news articles). By accessing a given topic, a user can further accessarticles that are associated with, or included within, that topic. Userscan post content in association to a topic, whereupon such content willbecome accessible to others viewing that topic. It will be appreciatedthat users can include individual persons as well as entities (e.g. newspublication entities). Furthermore, users may subscribe to individualtopics, and thereby have content from their subscribed topics presentedin their personal feeds. Thus, topics provide a classification mechanismfor organizing Internet content in the context of a social sharingplatform, that enables content of a given topic to be presentedalongside other content that is related to the same topic. In someimplementations, a topic can be implemented as a tag that can beutilized to surface content in association with that tag. Users caninteract with content or postings of a given topic, such as by postingcomments and/or providing social signals such as indicating endorsement(“like,” thumbs-up, upvote, etc.) or disapproval (thumbs-down,downvote), flagging (e.g. as abusive, inappropriate, etc.), etc. Thus,topics can also serve as conversation areas for users to express theirthoughts and reactions in relation to specific subjects.

One example of a topic, in accordance with implementations of thedisclosure, is the “Vibe” on the Yahoo! Newsroom platform. Anotherexample of a topic is the “subreddit” on the Reddit platform. Topics areuser-driven in the sense that users are able to post content inassociation with topics of their choosing. It should be appreciated thatthe content contained within a given topic can be any of various kindsof Internet content that users may interact with, including, withoutlimitation, articles (e.g. news, blog, etc.), images, videos, comments,discussion threads, reposts of content, etc.

In some implementations, the region of the interface occupied by icon102 can be configured to show a series of featured topics, one at atime, with others in the series being accessible in response to, forexample, swiping sideways. The featured topics can be recommended to auser based on a variety of factors, such as popularity (current ortrending), geo-location of the user, historical activity of the user,user preferences, etc. Furthermore, as discussed in further detailherein, topics may be recommended on the basis of their relationship toother topics to which the user has subscribed, as determined inaccordance with the methods described in the present disclosure.

With continued reference to FIG. 1, the icon 102 identifying the topic“Grammys” further includes an indication 104 of the number offollowers/subscribers to the topic. Additionally, a button (star) 106can be selected by the user in order to follow or subscribe to the topic“Grammys” By subscribing to a given topic, then that topic is associatedwith the user's account. Further, in some implementations, a combinedfeed of articles from the user's subscribed topics can be provided, e.g.in a Newsroom view that is accessed when the icon 116 is selected.

A search button 108 can be selected to enable a user to search fortopics by name. A post button 114 provides access to an interface forenabling the user to post a content item, such as a news article orother Internet content, to a chosen topic. Also shown in the illustratedimplementation are icons 110 and 112, which represent a “Photo fanatics”topic and a “Sinkholes” topic, respectively.

It will be appreciated that by selecting the icon representing aparticular topic, then access is provided to content that is within, orassociated with, that topic. For example, selecting the icon 102 willprovide access to articles that are classified within the “Grammys”topic. Selecting the icon 110 will provide access to articles within the“Photo fanatics” topic. Selecting the icon 112 will enable the user toview content within the “Sinkholes” topic.

Along the bottom portion of the interface shown is a series ofbuttons/icons for accessing different portions of the Yahoo! Newsroomapplication. In the illustrated view, the button 118 is currentlyselected, thereby accessing an “Explore” view that enables explorationof vibes/topics within the Newsroom platform. The button 116 can beselected to access a “Newsroom” view that provides a personalized feedof content based on the topics to which the user has subscribed. Thepersonalized feed features articles from topics to which the user hassubscribed. In various implementations, the feed can be furtherpersonalized based on various factors, such as the user's currentlocation, a current time of day, current popularity or trends withrespect to particular articles, the user's historical browsing historyof articles and preferences ascertained therefrom, etc.

The button 120 is selectable to access a view of the topics to which theuser has subscribed. This enables the user to delve specifically intoparticular topics that they are following, to view articles within aparticular topic. And the button 122 is selectable to access a profileview for the user, enabling view and editing of various personal profileinformation, such as user name, personal picture, background picture,residence, contact information, etc.

FIG. 2 illustrates an interface showing presentation of content within atopic, in accordance with implementations of the disclosure. Ascreenshot 200 is shown, illustrating a view of an interface forexploring content within a topic. In the illustrated implementation, theinterface is configured to show content within the topic “Animals in thenews.” A stream or feed is shown that is configured to present variouspreviews of articles/content within the “Animals in the news” topic. Itwill be appreciated that the stream can be scrolled to enable viewing ofadditional article previews. The previews are selectable to enableviewing of the entire articles/content which they represent. It will beappreciated that selection of a preview to view the actual article canbe considered as a “view” of the article for purposes of ascertaininguser activity. By way of example, the preview 202 is shown for a newsarticle entitled “Some DC schools cancel recess over escaped bobcat.”

The preview 202 includes a representative image 204, which can be animage from the article itself, that may be scaled, cropped, or otherwiseadjusted to fit within the context of the preview. In someimplementations, a representative video can be shown in the case of anarticle that includes a video. A caption 206 provides a briefdescription of the article, which may be a title or other representativetext from the article, such as a section heading, openingphrase/sentence, picture/video caption, etc. An indication of the numberof reactions/comments posted in response to the article is shown atreference 208. An indication of the number of “likes”/endorsements byusers is shown at reference 210. A comment 212 is displayed alongsidethe article preview, which is one of the comments which have been postedby the users. In some implementations, the comment shown can beperiodically cycled to display different ones of the comments that areassociated with the article.

The icon/button 214 is selectable to access an interface for posting acomment/reaction to the article. The icon 216 is selectable to access aninterface that enables reposting of the article to another topic/vibe.The icon 218 is selectable to indicate endorsement (or a “like”) of thearticle by the user.

FIG. 3 illustrates a method for identifying latent communities based ontopical activity, in accordance with implementations of the disclosure.Broadly speaking, it has been discovered that users participating in aplatform having a plurality of topics, as discussed above, can bedetermined to be members of certain latent communities. Thesecommunities are latent in the sense that they are not explicitlydefined, but can be inferred through analysis of the user activity andusage patterns occurring across topics.

At method operation 300, interaction data for a plurality of topics isobtained. The interaction data can include data describing any type ofinteraction occurring in relation to the topics, as a function of time(e.g. an activity amount on an hourly/daily/weekly basis). It will beappreciated that in various implementations, various types ofinteractions can be considered, alone or in combination, and may includeany of the user activity types described herein with respect to aplurality of topics. By way of example, without limitation, suchinteractions can include views, comments, likes, endorsements,thumbs-up, thumbs-down, upvote, downvote, shares, reposts, flags,moderator actions (e.g. removing flagged posts), etc. Thus, examples ofactivity amounts by time for a topic include hourly number of activeusers, daily number of comments, etc.

In further implementations, the interaction data can describe trends inactivity for a given topic, such as may be defined by a first orderderivative indicating the rate of change in an activity amount, or asecond order derivative indicating the trend in the rate of change.

At method operation 302, a covariance matrix is generated using theinteraction data. The covariance matrix is configured to mathematicallydescribe the covariance between each of the topics with each other. Thecovariance between two topics indicates the covariate correlationbetween them, with a higher correlation corresponding to a higher value.Thus in a general sense, the covariance matrix provides an indication ofthe behavioral similarity of each topic as compared to each other topicin the plurality of topics, with respect to the activity of interestoccurring over time.

By way of example, Table I below illustrates an example of interactiondata that is a time series of daily posts for Topics A, B, C, and D overthe course of four days.

TABLE I Oct. 21, Oct. 22, Oct. 23, Oct. 24, # Posts 2016 2016 2016 2016Topic A 200 123 120 123 Topic B 114 1215 1233 345 Topic C 123 954 994123 Topic D 152 23 21 12

Table II below illustrates a covariance matrix constructed from theabove data of Table I.

TABLE II Topic A Topic B Topic C Topic D Topic A [0.3, 0.2, 0.5, 0.1][0.4, 0.1, 0.1, 1.0] [0.5, 0.2, 0.1, 0.05] Topic B [0.3, 0.2, 0.5, 0.1][0.7, 0.9, 0.8, 0.7] [0.7, 0.1, 0.1, 0.1] Topic C [0.4, 0.1, 0.1, 1.0][0.7, 0.9, 0.8, 0.7] [0.9, 0.05, 0.05, 0.1] Topic D [0.5, 0.2, 0.1,0.05] [0.7, 0.1, 0.1, 0.1] [0.9, 0.05, 0.05, 0.1]

At method operation 304, a graphical lasso is applied to the covariancematrix to estimate a sparse graph. In the sparse graph, nodes correspondto the topics, and edges between the nodes have edge weights that are(at least approximately) defined by the values from the covariancematrix.

It will be appreciated that graphical lasso is one of many ways toestimate a sparse covariance matrix. Thus, while implementations of thepresent disclosure are described with reference to graphical lasso, itshould be appreciated that in other implementations, other techniquesfor estimating a sparse covariance matrix can be applied.

By way of example, additional methods for estimating a sparse covariancematrix are described in the following papers, the disclosures of whichare incorporated by reference:

Hsieh, C. J., Dhillon, I. S., Ravikumar, P. K. and Sustik, M. A., 2011.Sparse inverse covariance matrix estimation using quadraticapproximation. In Advances in neural information processing systems (pp.2330-2338).

Schäfer, J. and Strimmer, K., 2005. A shrinkage approach to large-scalecovariance matrix estimation and implications for functional genomics.Statistical applications in genetics and molecular biology, 4(1), p. 32.

Yuan, M., 2010. High dimensional inverse covariance matrix estimationvia linear programming Journal of Machine Learning Research, 11(August),pp. 2261-2286.

Cai, T., Liu, W. and Luo, X., 2011. A constrained l 1 minimizationapproach to sparse precision matrix estimation. Journal of the AmericanStatistical Association, 106(494), pp. 594-607.

Friedman, J., Hastie, T. and Tibshirani, R., 2008. Sparse inversecovariance estimation with the graphical lasso. Biostatistics, 9(3), pp.432-441.

At method operation 306, a stochastic block algorithm/model is appliedto the sparse graph, to partition the graph into clusters/communities oftopics. Broadly speaking, the stochastic block model randomly traversesthe sparse graph, and partitions the graph into different components bysevering some of the weaker or unnecessary edges. In doing so, thestochastic block model maximizes intra-community interactions (whichdefine the behavioral similarity of nodes within the same community,e.g. based on their covariance values) and minimizes inter-communityinteractions (which define the behavioral similarity of nodes fromdifferent communities, e.g. based on their covariance values). Thisresults in a high number of connections between nodes within a givencommunity and few connections between nodes of different communities.

The stochastic block algorithm computes these kinds of communitieswithout significantly disturbing the sparse graph's overall originalstructure. To do so, a metric of minimum description length is utilized.That is, the number of edges in the new graph is minimized as comparedto the original graph, while the difference between the sum of theweights of the edges in the original graph versus the new graph isminimized.

In some implementations, the stochastic block algorithm assigns softmembership to each topic with a posterior probability, and therefore itis possible to group a topic under several communities. Whereas in someimplementations, the stochastic block algorithm assigns hard membershipto each topic, such that each topic is a member of only one community.

The result of operations 302, 304, and 306 is a graph that identifiesvarious clusters/communities of topics. That is, topics which exhibitsimilar behavioral patterns for the activity (or activities) analyzedaccording to the foregoing operations will tend to be clustered as partof the same community. Thus, the result enables identification ofcommunities and the topics which constitute those communities.

At method operation 308, the identified communities are instantiated asobjects/entities on the social platform, to which users may subscribe orotherwise gain access, being generated based on the clusters of topicswhich have been identified. At method operation 310, users are providedaccess to these communities. For example, in some implementations, acommunity can be accessed through the interface, wherein accessing acommunity further enables access to the topics which are containedwithin that community, and likewise access to any of the topics in thecommunity further provides access to content/articles/postings withinthat topic.

Users may subscribe to a community, and in so doing, may be subscribedto all topics existing within that community, and may therefore receivein their personalized feed content from the various topics which are inthat community. Additionally, users may post to a community, which canbe configured to further effect posting to one or more of the topicswithin the community. In some implementations, a post to a community canbe selectively configured by the user to be associated with one or moreof the topics in that community.

It should be appreciated that the communities in the present disclosurehave been identified through an automated process that may beimplemented by at least one server computer. This is significant in thatthe communities are defined organically as a result of user activityoccurring within the plurality of topics, and are identified andgenerated without requiring human editorial activity. These communitiesare latent as they are not explicitly defined at the outset, but arediscovered through the methodology thus described. It will beappreciated that membership of topics within the communities may evolveover time, and thus, the particular topics within a given community canchange over time, and new communities may come into existence andexisting communities may cease to exist over time.

FIG. 4 is a graph visualizing a plurality of communities, in accordancewith implementations of the disclosure. In the illustratedimplementation, each node is a community, and the size of the communityindicates how many topics/vibes are in that community. The edge widthbetween communities graphically indicates how much the nodes in thecommunities interact (inter-community interactions). Thus, for example,the nodes in community “3” and community “15” have fewer interactionswith each other than the nodes in community “8” and community “10.”

FIG. 5 conceptually illustrates the grouping of topics into variouscommunities, in accordance with implementations of the disclosure. Asshown, various pieces of content 500 a-c are posted to various topics502, including topics T₁ to T₁₂. Users interact with the topics,including posting, viewing posts, commenting, endorsing, etc. Theseinteractions are analyzed to identify topics which exhibit similarbehavioral patterns over time. The topics which exhibit similarbehavioral patterns are grouped into communities. In the illustratedimplementation, a community C₁ is identified which includes topics T₁,T₂, T₄, and T₅; a community C₂ is identified which includes topics T₃,T₆, and T₇; and a community C₃ is identified which includes topics T₈,T₉, T₁₀, T₁₁, and T₁₂. The identification of these latent communitiescan be utilized to improve the user experience, such as by generatingcommunities on the platform which the user may access/subscribe,recommending posts to the user, or recommending topics to the user (e.g.recommending a topic that is in the same community as one to which theuser has subscribed).

The understanding of user behavior in accordance with implementations ofthe present disclosure can be utilized to provide community basedcontent recommendation solutions. In some implementations, specificposts (or articles, content, etc.) can be recommended to particularusers, at least in part, on the basis of community-defined parameters.For example, a post might be recommended to a user that is not withinany of the topics that the user is currently following, but which isnonetheless within a topic in the same identified community as one ormore of the topics that the user is currently following. The inclusionof such posts can effectively expand the corpus of posts that the systemmay draw upon for recommendation to the user, with a higher degree ofconfidence that such posts will be relevant to the user despite theirfalling outside of the user's explicitly subscribed topics.

In some implementations, a weighted ranking method that is applied torank posts for recommendation to the user can include one or moreweighting factors which are based on community affiliation. For example,a weighting factor for a given post can be assigned based on whether thegiven post is within a topic that is within a same community as topicsto which the user has subscribed. In some implementations, a weightingfactor can be assigned based on the strength of the intra-communityinteraction occurring between a topic to which the given post belongsand one or more topics to which the user has subscribed.

Throughout the present disclosure, reference is made to a user'ssubscriptions to specific topics. However, it will be appreciated that auser's topical interest can be inferred based on the user'sviewing/browsing history. And therefore, in all such implementations,the user's subscribed topics can be replaced or supplemented with topicsof interest that have been inferred from the user's activity.

In another application, the understanding of communities gained throughthe present methods can be used to recommend specific topics to a user.For example, a topic may be recommended to the user that is from thesame community as another topic to which the user has subscribed. Insome implementations, topics for recommendation are ranked based on thestrength of their intra-community interaction with other topics to whichthe user has subscribed. Thus, the system may recommend topics instrongly connected communities to which the user has subscribed.

In some implementations, the system may enable users to subscribe tocommunities, which are dynamic sets of changing topics. For example, a“Politics” community may contain several related topics such as “DonaldTrump,” “Hillary Clinton,” “Bill Clinton,” “Debate 2016,” etc. Bysubscribing to a community, the user may receive content in theirpersonalized feed from the various topics that are within the community,without having to subscribe on an individual basis to each of thevarious topics in the community. Additionally, the particular topicswithin the community are dynamic, and may change over time based on useractivity. For example, if the behavioral similarity of a given topic toother topics in the community reduces over time to the point that thetopic is no longer identified in the same cluster, accordance with themethods described above, then the topic would be removed from thecommunity. Conversely, a given topic's behavioral similarity to topicsof a community may increase to the point that the given topic isidentified in the same cluster and therefore added to the community. Ascommunity membership is dynamic, users are able to enjoy selections ofcontent from relevant topics in a seamless manner with minimal userconfiguration of topics required.

In some implementations, a post to one topic may be automatically postedto one or more other topics within the same community. In someimplementations, a post to one topic is posted to another topic withinits community when the topics exhibit an intra-community interactionabove a predefined threshold. By way of example, a post to a “DonaldTrump” topic can be posted to other topics such as “Elections” thatbelong to same community with higher confidence. This avoids the problemof users having to manually “repost” a post from the “Donald Trump”topic to the “Elections” topic. As noted above, one could also post tothe relevant community instead of a specific topic, and thereby post toall of the topics in that community.

In some implementations, community affiliation can be used to guideplacement of topics when presented to a user. For example, topics fromthe same community can be placed in proximity to each other. Or thesystem can be configured to avoid placing unrelated topics next to eachother. For example, topics such as “Yahoo” and “Google” should not beplaced next to topics such as “War on Drugs” and “Al Qaeda.”

By automatically learning the structure of the underlying latentcommunities, system performance is improved in terms of bothrecommending topics that a user might be interesting in following and inranking user posts. These improvements lead to increased productperformance and better user retention on the social platform.

Because it observes user activity over time, i.e., it istemporally-dynamic, the system can capture the dynamics of anever-evolving social network. Existing systems today require the use ofeditors to accomplish this. Having an algorithmic-based solution isfaster and more cost efficient.

Modeling and visualization of the latent communities comprising platformusers and topics can be used both for informational dashboards andexploratory analysis. For example, editorial views can be providedshowing the relationships amongst topics, the relative strengths of suchrelationships, the trends in the relative strengths, the composition ofcommunities, the trends in user activity by topic and community, etc.

In some implementations, analysis of social signals and moderatoractivity such as removing abusive posts might lead to analyses thatgroup topics that tend to have such activity in the same community. Thissystem could thus use this information as a way to curate topics andperhaps make them less visible on the platform.

In some implementations, the system populates a community with certaintopics/vibes, and the user has the option of removing or adding newtopics to the community. For example, users could graphically drag/droptopics in or out of a community. This could be monitored as feedback tothe system regarding whether the user agrees that a given topic shouldbe included in a community, and be used to adjust the modeling system.

In some implementations, the name of the community itself can beassigned by the system, assigned by an editor, and/or determined oredited by a user. There can be dynamically changing labels forcommunities, e.g. based on user determination and topic inclusion in thecommunities.

In some implementations, when a user subscribes to a community, the useris then automatically subscribed to various topics within thatcommunity.

In some implementations, initially, the user can be presented with anumber of suggested communities, which have been determined throughanalysis of the topics. This serves to ease the initial setup of theapplication, so that the user does not have to browse through a largenumber of topics, but can select from a more limited number ofcommunities.

As noted above, similar topics can be shown next to each other.Additionally, a hierarchical structure of topics can be systematicallygenerated based on identified communities. For example, a technologycommunity might include internet technology topics, which in turn mightinclude travel internet and ecommerce internet topics.

FIG. 6 illustrates a system for providing community-based solutions in asocial sharing platform, in accordance with implementations of thedisclosure. Broadly speaking, the system can be configured to performany of the methods described in accordance with implementations of thepresent disclosure. A client device 600 is operated by a user to accessa social sharing platform that uses a plurality of topics. The clientdevice 600 executes an application 602 (which in some implementations,may be a browser application or a web application) that is configured torender to the display 604 of the client device 600 an interface forinteracting with the social sharing platform. The application 602 maycommunicate over a network 606 (e.g. the Internet) with a socialapplication server 608 to obtain data so that the user may access thesocial sharing platform, including accessing topics, content withintopics, a personalized feed, etc.

It will be appreciated that in some implementations, content can beobtained from a separate content server 610 for rendering in the contextof the interface that is rendered on the client device 600. For example,in a given topic, and preview of an article from a 3^(rd) party newssource may be provided, and accessing the article may redirect to obtaincontent from the 3^(rd) party news source's content server.

A storage 620 is configured to store user data 622, which includes userprofile information as well as users' platform related information, suchas the topics to which users are subscribed. Topics data 624 includesdata that defines the contents of a given topic, including identifiersof content that are included in the given topic. Interaction data 626includes data that describes activity occurring within each of thetopics. Communities data 628 includes data defining the communities onthe platform, including identification of which topics are members ofwhich communities.

A community analyzer 612 is configured to analyze the interaction data626 in accordance with methods described above, to determine clusters oftopics and generate/edit communities on the platform. A covariance logic614 is configured to determine a covariance amongst the topics, forexample by determining a covariance matrix for the topics. A sparsegraph estimator 616 can be configured to apply a graphical lasso orother technique to estimate a sparse graph based on the covariance. Astochastic block model 618 is applied to identify clusters of topics,which serve to define communities on the social sharing platform. Asnoted above, these identified communities can be leveraged in variousways to improve the user experience and the platform's performance.

FIG. 7 illustrates an implementation of a general computer systemdesignated 700. The computer system 700 can include a set ofinstructions that can be executed to cause the computer system 700 toperform any one or more of the methods or computer based functionsdisclosed herein. The computer system 700 may operate as a standalonedevice or may be connected, e.g., using a network, to other computersystems or peripheral devices.

In a networked deployment, the computer system 700 may operate in thecapacity of a server or as a client user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 700 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a mobile device, a palmtop computer, a laptopcomputer, a desktop computer, a communications device, a wirelesstelephone, a land-line telephone, a control system, a camera, a scanner,a facsimile machine, a printer, a pager, a personal trusted device, aweb appliance, a network router, switch or bridge, or any other machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine. In a particularimplementation, the computer system 700 can be implemented usingelectronic devices that provide voice, video or data communication.Further, while a single computer system 700 is illustrated, the term“system” shall also be taken to include any collection of systems orsub-systems that individually or jointly execute a set, or multiplesets, of instructions to perform one or more computer functions.

As illustrated in FIG. 7, the computer system 700 may include aprocessor 702, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 702 may be a component ina variety of systems. For example, the processor 702 may be part of astandard personal computer or a workstation. The processor 702 may beone or more general processors, digital signal processors, applicationspecific integrated circuits, field programmable gate arrays, servers,networks, digital circuits, analog circuits, combinations thereof, orother now known or later developed devices for analyzing and processingdata. The processor 702 may implement a software program, such as codegenerated manually (i.e., programmed).

The computer system 700 may include a memory 704 that can communicatevia a bus 708. The memory 704 may be a main memory, a static memory, ora dynamic memory. The memory 704 may include, but is not limited tocomputer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In oneimplementation, the memory 704 includes a cache or random access memoryfor the processor 702. In alternative implementations, the memory 704 isseparate from the processor 702, such as a cache memory of a processor,the system memory, or other memory. The memory 704 may be an externalstorage device or database for storing data. Examples include a harddrive, compact disc (“CD”), digital video disc (“DVD”), memory card,memory stick, floppy disc, universal serial bus (“USB”) memory device,or any other device operative to store data. The memory 704 is operableto store instructions executable by the processor 702. The functions,acts or tasks illustrated in the figures or described herein may beperformed by the programmed processor 702 executing the instructionsstored in the memory 704. The functions, acts or tasks are independentof the particular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 700 may further include a display unit710, such as a liquid crystal display (LCD), an organic light emittingdiode (OLED), a flat panel display, a solid state display, a cathode raytube (CRT), a projector, a printer or other now known or later developeddisplay device for outputting determined information. The display 710may act as an interface for the user to see the functioning of theprocessor 702, or specifically as an interface with the software storedin the memory 704 or in the drive unit 706.

Additionally or alternatively, the computer system 700 may include aninput device 712 configured to allow a user to interact with any of thecomponents of system 700. The input device 712 may be a number pad, akeyboard, or a cursor control device, such as a mouse, or a joystick,touch screen display, remote control or any other device operative tointeract with the computer system 700.

The computer system 700 may also or alternatively include a disk oroptical drive unit 706. The disk drive unit 706 may include acomputer-readable medium 722 in which one or more sets of instructions724, e.g. software, can be embedded. Further, the instructions 724 mayembody one or more of the methods or logic as described herein. Theinstructions 724 may reside completely or partially within the memory704 and/or within the processor 702 during execution by the computersystem 700. The memory 704 and the processor 702 also may includecomputer-readable media as discussed above.

In some systems, a computer-readable medium 722 includes instructions724 or receives and executes instructions 724 responsive to a propagatedsignal so that a device connected to a network 726 can communicatevoice, video, audio, images or any other data over the network 726.Further, the instructions 724 may be transmitted or received over thenetwork 726 via a communication port or interface 720, and/or using abus 708. The communication port or interface 720 may be a part of theprocessor 702 or may be a separate component. The communication port 720may be created in software or may be a physical connection in hardware.The communication port 720 may be configured to connect with a network726, external media, the display 710, or any other components in system700, or combinations thereof. The connection with the network 726 may bea physical connection, such as a wired Ethernet connection or may beestablished wirelessly as discussed below. Likewise, the additionalconnections with other components of the system 700 may be physicalconnections or may be established wirelessly. The network 726 mayalternatively be directly connected to the bus 708.

While the computer-readable medium 722 is shown to be a single medium,the term “computer-readable medium” may include a single medium ormultiple media, such as a centralized or distributed database, and/orassociated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” may also include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein. The computer-readable medium 722 may be non-transitory, and maybe tangible.

The computer-readable medium 722 can include a solid-state memory suchas a memory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium 722 can be a randomaccess memory or other volatile re-writable memory. Additionally oralternatively, the computer-readable medium 722 can include amagneto-optical or optical medium, such as a disk or tapes or otherstorage device to capture carrier wave signals such as a signalcommunicated over a transmission medium. A digital file attachment to ane-mail or other self-contained information archive or set of archivesmay be considered a distribution medium that is a tangible storagemedium. Accordingly, the disclosure is considered to include any one ormore of a computer-readable medium or a distribution medium and otherequivalents and successor media, in which data or instructions may bestored.

In an alternative implementation, dedicated hardware implementations,such as application specific integrated circuits, programmable logicarrays and other hardware devices, can be constructed to implement oneor more of the methods described herein. Applications that may includethe apparatus and systems of various implementations can broadly includea variety of electronic and computer systems. One or moreimplementations described herein may implement functions using two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals that can be communicated between and throughthe modules, or as portions of an application-specific integratedcircuit. Accordingly, the present system encompasses software, firmware,and hardware implementations.

The computer system 700 may be connected to one or more networks 726.The network 726 may define one or more networks including wired orwireless networks. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, suchnetworks may include a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to TCP/IP based networking protocols. Thenetwork 726 may include wide area networks (WAN), such as the Internet,local area networks (LAN), campus area networks, metropolitan areanetworks, a direct connection such as through a Universal Serial Bus(USB) port, or any other networks that may allow for data communication.The network 726 may be configured to couple one computing device toanother computing device to enable communication of data between thedevices. The network 726 may generally be enabled to employ any form ofmachine-readable media for communicating information from one device toanother. The network 726 may include communication methods by whichinformation may travel between computing devices. The network 726 may bedivided into sub-networks. The sub-networks may allow access to all ofthe other components connected thereto or the sub-networks may restrictaccess between the components. The network 726 may be regarded as apublic or private network connection and may include, for example, avirtual private network or an encryption or other security mechanismemployed over the public Internet, or the like.

In accordance with various implementations of the present disclosure,the methods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedimplementation, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular implementations with reference toparticular standards and protocols, the disclosure is not limited tosuch standards and protocols. For example, standards for Internet andother packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML,HTTP) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted exceptin light of the attached claims and their equivalents.

What is claimed is:
 1. A method implemented by at least one servercomputer, comprising: providing, over the Internet, access to aplurality of topics, wherein each topic includes, and provides accessto, a plurality of posted items; recording interaction data for theplurality of topics, the interaction data identifying user activityoccurring within each of the plurality of topics; analyzing theinteraction data to identify clusters of topics that exhibit similaractivity-behavioral patterns; for each cluster of topics, generating acommunity that includes topics in the cluster; and providing, over theInternet, access to the communities, wherein accessing a given communityprovides access to topics included in that community, which provideaccess to posted items that are included in the topics within thatcommunity.
 2. The method of claim 1, comprising: generating a covariancematrix using the interaction data.
 3. The method of claim 2, comprising:applying a graphical lasso to the covariance matrix.
 4. The method ofclaim 1, wherein the interaction data defines a time-dependent metricfor user activity within the plurality of topics, and wherein a similarbehavioral pattern for a given cluster of topics is defined by similarchanges in the time-dependent metric occurring over time for the givencluster of topics.
 5. The method of claim 4, wherein the interactiondata includes a number of one or more of active users, posted items,comments, votes, positive endorsements, negative endorsements, abuseflags, moderator actions.
 6. The method of claim 1, wherein providingaccess to a given topic includes providing an interface for postingitems within the given topic, and for interacting with posted items inthe given topic, and wherein the interaction data includes data obtainedvia the interface.
 7. The method of claim 1, wherein the plurality ofposted items include news articles.
 8. A method implemented by at leastone server computer, comprising: providing, over the Internet, access toa plurality of topics, wherein each topic includes, and provides accessto, a plurality of posted items; recording interaction data for theplurality of topics, the interaction data identifying user activityoccurring within each of the plurality of topics; analyzing theinteraction data to identify clusters of topics that exhibit similarbehavioral patterns; using the clusters of topics to identify topics forrecommendation to a user; and presenting, over the Internet, the topicsidentified for recommendation in a session for the user.
 9. The methodof claim 8, wherein the topics for recommendation are identified basedon identifying a topic to which the user has subscribed.
 10. The methodof claim 9, wherein the topics for recommendation are identified basedon identifying one or more topics that are within a same cluster as thetopic to which the user has subscribed.
 11. The method of claim 8,comprising: generating a covariance matrix using the interaction data;and applying a graphical lasso to the covariance matrix.
 12. The methodof claim 8, wherein the interaction data defines a time-dependent metricfor user activity within the plurality of topics, and wherein a similarbehavioral pattern for a given cluster of topics is defined by similarchanges in the time-dependent metric occurring over time for the givencluster of topics.
 13. The method of claim 12, wherein the interactiondata includes a number of one or more of active users, posted items,comments, votes, positive endorsements, negative endorsements, flags,moderator actions.
 14. The method of claim 8, wherein providing accessto a given topic includes providing an interface for posting itemswithin the given topic, and for interacting with posted items in thegiven topic, and wherein the interaction data includes data obtained viathe interface.
 15. The method of claim 8, wherein the plurality ofposted items include news articles.
 16. A method implemented by at leastone server computer, comprising: providing access to a plurality oftopics, wherein each topic includes, and provides access to, a pluralityof posted items; recording interaction data for the plurality of topics,the interaction data identifying user activity occurring within each ofthe plurality of topics; analyzing the interaction data to identifyclusters of topics that exhibit similar behavioral patterns; and usingthe clusters of topics to recommend specific user-posted items.
 17. Themethod of claim 16, wherein using the clusters of topics to recommendspecific user-posted items includes identifying a first topic to which auser has subscribed.
 18. The method of claim 17, wherein using theclusters of topics to recommend specific user-posted items includesidentifying a second topic that is within a same cluster as the firsttopic.
 19. The method of claim 18, wherein using the clusters of topicsto recommend specific user-posted items includes recommending in asession of the user a posted item from the second topic.
 20. The methodof claim 16, wherein the interaction data defines a time-dependentmetric for user activity within the plurality of topics, and wherein asimilar behavioral pattern for a given cluster of topics is defined bysimilar changes in the time-dependent metric occurring over time for thegiven cluster of topics.